{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_1.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}18 Aug 2020, 06:19:17
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_raw.dta", clear
. {c )-}
{txt}
{com}. 
. if ("`model'" == "mi") {c -(}
.         use "${c -(}home_dir{c )-}/data/processed/suicide_reg_v1_imputed_M10.dta", clear  
. {c )-}
{txt}
{com}. 
. * for now, we use the following simple survey weights 
. if ("`model'" == "mi") {c -(}
.         mi svyset `geo_type' [pw=ObsWgt0] 
. {c )-}
{txt}
{com}. else {c -(}
.         svyset `geo_type' [pw=ObsWgt0]  

      {txt}pweight:{col 16}{res}ObsWgt0
          {txt}VCE:{col 16}{res}linearized
  {txt}Single unit:{col 16}{res}missing
     {txt}Strata 1:{col 16}<one>
         SU 1:{col 16}{res}county
        {txt}FPC 1:{col 16}<zero>
{p2colreset}{...}
{com}. {c )-}
{txt}
{com}. 
. * margins for each category
. program margin_interact 
{txt}  1{com}.         args X Y k model
{txt}  2{com}.         sum `X', d 
{txt}  3{com}.         local gap = (`r(max)' - `r(min)') / `k' 
{txt}  4{com}.         if ("`model'" == "logit") {c -(}
{txt}  5{com}.                 margin `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  6{com}.         {c )-} 
{txt}  7{com}.         else if ("`model'" == "mi") {c -(}
{txt}  8{com}.                 mimrgns `Y', at(`X' = (`r(min)' (`gap') `r(max)')) predict(pr)
{txt}  9{com}.         {c )-}       
{txt} 10{com}. end 
{txt}
{com}. 
. program mchange_mi
{txt}  1{com}.         args X k model
{txt}  2{com}.         if ("`model'" == "logit") {c -(}
{txt}  3{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt}  4{com}.                         sum `X' if e(sample), d 
{txt}  5{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt}  6{com}.                         mlincom  2 - 1, decimal(7) stat(all)    
{txt}  7{com}.                 {c )-} 
{txt}  8{com}.                 else if ("`k'" == "binary") {c -(}
{txt}  9{com}.                         sum `X' if e(sample), d 
{txt} 10{com}.                         margin, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 11{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 12{com}.                 {c )-} 
{txt} 13{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 14{com}.                         margin `X' if e(sample)==1 , at() pwcompare predict(pr) 
{txt} 15{com}.                 {c )-}
{txt} 16{com}.         {c )-}
{txt} 17{com}. 
.         else if ("`model'" == "mi") {c -(}
{txt} 18{com}. 
.                 if ("`k'" == "continuous") {c -(}
{txt} 19{com}.                         sum `X' , d 
{txt} 20{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 21{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 22{com}.                 {c )-} 
{txt} 23{com}.                 else if ("`k'" == "binary") {c -(}
{txt} 24{com}.                         sum `X' , d 
{txt} 25{com}.                         mimrgns, at(`X' = (`r(min)' `r(max)')) post predict(pr)
{txt} 26{com}.                         mlincom  2 - 1, decimal(7) stat(all)
{txt} 27{com}.                 {c )-} 
{txt} 28{com}.                 else if ("`k'" == "categorical") {c -(}
{txt} 29{com}.                         mimrgns `X' , at()   pwcompare predict(pr)      
{txt} 30{com}.                 {c )-}
{txt} 31{com}.         {c )-}
{txt} 32{com}. end
{txt}
{com}. 
. * create some dummy codings
. tab Race5, gen(race5_nh)

      {txt}Race5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  9,802,895       79.53       79.53
{txt}          2 {c |}{res}  1,330,104       10.79       90.32
{txt}          3 {c |}{res}    260,664        2.11       92.44
{txt}          4 {c |}{res}    250,301        2.03       94.47
{txt}          5 {c |}{res}    681,739        5.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename race5_nh1 White_nh 
{res}{txt}
{com}.         rename race5_nh2 Black_nh 
{res}{txt}
{com}.         rename race5_nh3 AIAN_nh 
{res}{txt}
{com}.         rename race5_nh4 AsPI_nh 
{res}{txt}
{com}.         rename race5_nh5 Hispanic
{res}{txt}
{com}. 
. tab MarStat5, gen(ms)

   {txt}MarStat5 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  6,880,735       55.83       55.83
{txt}          2 {c |}{res}    924,215        7.50       63.32
{txt}          3 {c |}{res}  1,265,861       10.27       73.60
{txt}          4 {c |}{res}    237,203        1.92       75.52
{txt}          5 {c |}{res}  3,017,228       24.48      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,242      100.00
{txt}
{com}.         rename ms1 Marrd5
{res}{txt}
{com}.         rename ms2 Widow5
{res}{txt}
{com}.         rename ms3 Divor5
{res}{txt}
{com}.         rename ms4 Separ5
{res}{txt}
{com}.         rename ms5 NvMar5
{res}{txt}
{com}. 
. tab AgeGrp4, gen(ag) 

    {txt}AgeGrp4 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}  1,930,142       15.66       15.66
{txt}          2 {c |}{res}  3,549,392       28.80       44.46
{txt}          3 {c |}{res}  4,304,660       34.92       79.38
{txt}          4 {c |}{res}  2,541,509       20.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res} 12,325,703      100.00
{txt}
{com}.         rename ag1 Age_15_24
{res}{txt}
{com}.         rename ag2 Age_25_44
{res}{txt}
{com}.         rename ag3 Age_45_64
{res}{txt}
{com}.         rename ag4 Age_65_Up
{res}{txt}
{com}. 
. destring St, replace 
{txt}St: all characters numeric; {res}replaced {txt}as {res}byte
{txt}(16685 missing values generated)
{res}{txt}
{com}. 
. * set-up equations
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Cath Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local religion Rat_GC_ProE Rat_GC_ProM Rat_GC_ProB Rat_GC_Jew Rat_GC_Oth
{txt}
{com}. local contextual_control Rat_Poverty Rat_Mig_Cum Pop_Den
{txt}
{com}. 
. local demographics_raw i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
{txt}
{com}. local demographics_same i.Female c.std_same_prop_Sex i.AgeGrp4 c.std_same_prop_AgeGrp4 i.Race5 c.std_same_prop_Race5 i.BornUSA c.std_same_prop_BornUSA i.MarStat5 c.std_same_prop_MarStat5 
{txt}
{com}. local demographics_inter i.Female##c.std_same_prop_Sex i.AgeGrp4##c.std_same_prop_AgeGrp4 i.Race5##c.std_same_prop_Race5 i.BornUSA##c.std_same_prop_BornUSA i.MarStat5##c.std_same_prop_MarStat5
{txt}
{com}. 
. 
. if (`model_version' == 1){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' 
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 
. {c )-}
{txt}
{com}. if (`model_version' == 2){c -(}
.         local model_eq i.Year `demographics_raw' `contextual_control' `religion' i.UnEmpl c.RAT_UnEmpl i.PhysProb c.RAT_PhysProb
.         local margin_demographics Female RAT_Female AgeGrp4 RAT_AgeGrp4_2 RAT_AgeGrp4_3 RAT_AgeGrp4_4 Race5 RAT_Race5_2 RAT_Race5_3 RAT_Race5_4 RAT_Race5_5 BornUSA RAT_BornUSA MarStat5 RAT_MarStat5_2 RAT_MarStat5_3 RAT_MarStat5_4 RAT_MarStat5_5 UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 3){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' 
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 
. {c )-}
{txt}
{com}. if (`model_version' == 4){c -(}
.         local model_eq i.Year `demographics_same' `contextual_control' `religion' i.UnEmpl c.std_same_prop_UnEmpl i.PhysProb c.std_same_prop_PhysProb
.         local margin_demographics Female std_same_prop_Sex AgeGrp4 std_same_prop_AgeGrp4 Race5 std_same_prop_Race5 BornUSA std_same_prop_BornUSA MarStat5 std_same_prop_MarStat5 UnEmpl std_same_prop_UnEmpl PhysProb std_same_prop_PhysProb
. {c )-}
{txt}
{com}. if (`model_version' == 5){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' 
. {c )-}
{txt}
{com}. if (`model_version' == 6){c -(}
.         local model_eq i.Year `demographics_inter' `contextual_control' `religion' i.UnEmpl##c.std_same_prop_UnEmpl i.PhysProb##c.std_same_prop_PhysProb
. {c )-}       
{txt}
{com}. * test how long it would take.
. * mi estimate: svy: mean Suic 
. * local demographics i.Female c.RAT_Female i.AgeGrp4 c.RAT_AgeGrp4_2 c.RAT_AgeGrp4_3 c.RAT_AgeGrp4_4 i.Race5 c.RAT_Race5_2 c.RAT_Race5_3 c.RAT_Race5_4 c.RAT_Race5_5 i.BornUSA c.RAT_BornUSA i.MarStat5 c.RAT_MarStat5_2 c.RAT_MarStat5_3 c.RAT_MarStat5_4 c.RAT_MarStat5_5
. * mi estimate: svy: logit Suic i.St `demographics' UnEmpl RAT_UnEmpl PhysProb RAT_PhysProb
. 
. * main effects : margins
. if ("`model'" == "mi"){c -(}
. 
.         mi estimate: svy: logit Suic i.St `model_eq', or 
.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 estimates restore m1
.                 mchange_mi Female "binary" "mi"
.                 estimates restore m1
.                 mchange_mi AgeGrp4 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi Race5 "categorical" "mi"
.                 estimates restore m1
.                 mchange_mi BornUSA "binary" "mi"
.                 estimates restore m1
.                 mchange_mi MarStat5 "categorical" "mi"
.                 
.                 if (`model_version' == 1 | `model_version' == 2) {c -(}
.                         estimates restore m1
.                         mchange_mi RAT_Female "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_AgeGrp4_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_Race5_5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_2 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_3 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_MarStat5_5 "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 3 | `model_version' == 4) {c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_Sex "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_AgeGrp4 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_Race5 "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_BornUSA "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_MarStat5 "continuous" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2 | `model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi UnEmpl "categorical" "mi"
.                         estimates restore m1
.                         mchange_mi PhysProb "categorical" "mi"
.                 {c )-}
.         
.                 if (`model_version' == 2){c -(}
.                         estimates restore m1
.                         mchange_mi RAT_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi RAT_PhysProb "continuous" "mi"
.                 {c )-}
.                 if (`model_version' == 4){c -(}
.                         estimates restore m1
.                         mchange_mi std_same_prop_UnEmpl "continuous" "mi"
.                         estimates restore m1
.                         mchange_mi std_same_prop_PhysProb "continuous" "mi"
.                 {c )-}               
.         {c )-}
. {c )-}
{txt}
{com}. 
. if ("`model'" == "logit"){c -(}
.         svy: logit Suic i.St `model_eq', or 
{txt}(running {bf:logit} on estimation sample)
{res}
{txt}Survey: Logistic regression

{col 1}Number of strata{col 20}= {res}        1{txt}{col 47}Number of obs{col 65}= {res}  11,813,849
{txt}{col 1}Number of PSUs{col 20}= {res}      918{txt}{col 47}Population size{col 65}={res}  418,785,985
{txt}{col 47}Design df{col 65}= {res}         917
{txt}{col 47}F({res}  55{txt},{res}    863{txt}){col 65}= {res}      589.33
{txt}{col 47}Prob > F{col 65}= {res}      0.0000

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}  Linearized
{col 1}        Suic{col 14}{c |} Odds Ratio{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}St {c |}
{space 10}8  {c |}{col 14}{res}{space 2} .9635297{col 26}{space 2} .0932736{col 37}{space 1}   -0.38{col 46}{space 3}0.701{col 54}{space 4} .7968131{col 67}{space 3} 1.165128
{txt}{space 9}13  {c |}{col 14}{res}{space 2} .7336389{col 26}{space 2} .0760491{col 37}{space 1}   -2.99{col 46}{space 3}0.003{col 54}{space 4} .5985909{col 67}{space 3}  .899155
{txt}{space 9}21  {c |}{col 14}{res}{space 2} .6576575{col 26}{space 2} .0652508{col 37}{space 1}   -4.22{col 46}{space 3}0.000{col 54}{space 4} .5412956{col 67}{space 3} .7990336
{txt}{space 9}24  {c |}{col 14}{res}{space 2} .6107932{col 26}{space 2} .0631802{col 37}{space 1}   -4.77{col 46}{space 3}0.000{col 54}{space 4} .4985743{col 67}{space 3} .7482703
{txt}{space 9}25  {c |}{col 14}{res}{space 2} .5145834{col 26}{space 2} .0528631{col 37}{space 1}   -6.47{col 46}{space 3}0.000{col 54}{space 4} .4206263{col 67}{space 3} .6295281
{txt}{space 9}34  {c |}{col 14}{res}{space 2} .5479422{col 26}{space 2} .0610322{col 37}{space 1}   -5.40{col 46}{space 3}0.000{col 54}{space 4}  .440351{col 67}{space 3} .6818214
{txt}{space 9}35  {c |}{col 14}{res}{space 2} .8848342{col 26}{space 2} .1302833{col 37}{space 1}   -0.83{col 46}{space 3}0.406{col 54}{space 4} .6627734{col 67}{space 3} 1.181296
{txt}{space 9}37  {c |}{col 14}{res}{space 2} .7590376{col 26}{space 2} .0796788{col 37}{space 1}   -2.63{col 46}{space 3}0.009{col 54}{space 4}   .61772{col 67}{space 3} .9326849
{txt}{space 9}40  {c |}{col 14}{res}{space 2} .5623555{col 26}{space 2} .0602284{col 37}{space 1}   -5.37{col 46}{space 3}0.000{col 54}{space 4}   .45575{col 67}{space 3} .6938974
{txt}{space 9}41  {c |}{col 14}{res}{space 2} .7996948{col 26}{space 2} .0741439{col 37}{space 1}   -2.41{col 46}{space 3}0.016{col 54}{space 4} .6666541{col 67}{space 3} .9592857
{txt}{space 9}44  {c |}{col 14}{res}{space 2} .5694465{col 26}{space 2} .0580109{col 37}{space 1}   -5.53{col 46}{space 3}0.000{col 54}{space 4} .4662559{col 67}{space 3} .6954749
{txt}{space 9}45  {c |}{col 14}{res}{space 2} .6933312{col 26}{space 2} .0743654{col 37}{space 1}   -3.41{col 46}{space 3}0.001{col 54}{space 4} .5617225{col 67}{space 3} .8557752
{txt}{space 9}49  {c |}{col 14}{res}{space 2} 1.605098{col 26}{space 2} .2724564{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} 1.150333{col 67}{space 3} 2.239645
{txt}{space 9}51  {c |}{col 14}{res}{space 2}   .81796{col 26}{space 2} .0819022{col 37}{space 1}   -2.01{col 46}{space 3}0.045{col 54}{space 4} .6720301{col 67}{space 3} .9955782
{txt}{space 9}55  {c |}{col 14}{res}{space 2} .6516575{col 26}{space 2} .0653655{col 37}{space 1}   -4.27{col 46}{space 3}0.000{col 54}{space 4} .5352115{col 67}{space 3} .7934386
{txt}{space 12} {c |}
{space 8}Year {c |}
{space 7}2006  {c |}{col 14}{res}{space 2}  .938924{col 26}{space 2} .0167542{col 37}{space 1}   -3.53{col 46}{space 3}0.000{col 54}{space 4}  .906612{col 67}{space 3} .9723876
{txt}{space 7}2007  {c |}{col 14}{res}{space 2} 1.009367{col 26}{space 2} .0185152{col 37}{space 1}    0.51{col 46}{space 3}0.611{col 54}{space 4} .9736758{col 67}{space 3} 1.046366
{txt}{space 7}2008  {c |}{col 14}{res}{space 2} 1.011261{col 26}{space 2} .0199942{col 37}{space 1}    0.57{col 46}{space 3}0.571{col 54}{space 4} .9727732{col 67}{space 3} 1.051272
{txt}{space 7}2009  {c |}{col 14}{res}{space 2}   1.0521{col 26}{space 2} .0209435{col 37}{space 1}    2.55{col 46}{space 3}0.011{col 54}{space 4}  1.01179{col 67}{space 3} 1.094016
{txt}{space 7}2010  {c |}{col 14}{res}{space 2} 1.053507{col 26}{space 2} .0222506{col 37}{space 1}    2.47{col 46}{space 3}0.014{col 54}{space 4} 1.010731{col 67}{space 3} 1.098092
{txt}{space 7}2011  {c |}{col 14}{res}{space 2} 1.103234{col 26}{space 2} .0249025{col 37}{space 1}    4.35{col 46}{space 3}0.000{col 54}{space 4} 1.055428{col 67}{space 3} 1.153205
{txt}{space 12} {c |}
{space 4}1.Female {c |}{col 14}{res}{space 2} .2472798{col 26}{space 2} .0037544{col 37}{space 1}  -92.03{col 46}{space 3}0.000{col 54}{space 4} .2400203{col 67}{space 3} .2547589
{txt}{space 2}RAT_Female {c |}{col 14}{res}{space 2}  .985055{col 26}{space 2} .0080294{col 37}{space 1}   -1.85{col 46}{space 3}0.065{col 54}{space 4} .9694224{col 67}{space 3}  1.00094
{txt}{space 12} {c |}
{space 5}AgeGrp4 {c |}
{space 10}2  {c |}{col 14}{res}{space 2}  2.12972{col 26}{space 2} .0479247{col 37}{space 1}   33.60{col 46}{space 3}0.000{col 54}{space 4} 2.037712{col 67}{space 3} 2.225882
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 2.443766{col 26}{space 2} .0599736{col 37}{space 1}   36.41{col 46}{space 3}0.000{col 54}{space 4} 2.328854{col 67}{space 3} 2.564348
{txt}{space 10}4  {c |}{col 14}{res}{space 2} 1.983641{col 26}{space 2} .0539684{col 37}{space 1}   25.18{col 46}{space 3}0.000{col 54}{space 4} 1.880503{col 67}{space 3} 2.092436
{txt}{space 12} {c |}
RAT_AgeGrp~2 {c |}{col 14}{res}{space 2} .9868592{col 26}{space 2} .0038867{col 37}{space 1}   -3.36{col 46}{space 3}0.001{col 54}{space 4} .9792607{col 67}{space 3} .9945166
{txt}RAT_AgeGrp~3 {c |}{col 14}{res}{space 2}  .984146{col 26}{space 2} .0041916{col 37}{space 1}   -3.75{col 46}{space 3}0.000{col 54}{space 4}  .975954{col 67}{space 3} .9924067
{txt}RAT_AgeGrp~4 {c |}{col 14}{res}{space 2} 1.013471{col 26}{space 2} .0066801{col 37}{space 1}    2.03{col 46}{space 3}0.043{col 54}{space 4} 1.000445{col 67}{space 3} 1.026666
{txt}{space 12} {c |}
{space 7}Race5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} .3621639{col 26}{space 2} .0095007{col 37}{space 1}  -38.72{col 46}{space 3}0.000{col 54}{space 4} .3439902{col 67}{space 3} .3812978
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 1.012912{col 26}{space 2} .0783954{col 37}{space 1}    0.17{col 46}{space 3}0.868{col 54}{space 4} .8701721{col 67}{space 3} 1.179067
{txt}{space 10}4  {c |}{col 14}{res}{space 2}  .660072{col 26}{space 2} .0304965{col 37}{space 1}   -8.99{col 46}{space 3}0.000{col 54}{space 4} .6028542{col 67}{space 3} .7227205
{txt}{space 10}5  {c |}{col 14}{res}{space 2} .4911386{col 26}{space 2} .0200326{col 37}{space 1}  -17.43{col 46}{space 3}0.000{col 54}{space 4} .4533559{col 67}{space 3} .5320702
{txt}{space 12} {c |}
{space 1}RAT_Race5_2 {c |}{col 14}{res}{space 2} 1.003629{col 26}{space 2} .0012016{col 37}{space 1}    3.03{col 46}{space 3}0.003{col 54}{space 4} 1.001274{col 67}{space 3}  1.00599
{txt}{space 1}RAT_Race5_3 {c |}{col 14}{res}{space 2} 1.009382{col 26}{space 2} .0034478{col 37}{space 1}    2.73{col 46}{space 3}0.006{col 54}{space 4} 1.002638{col 67}{space 3} 1.016171
{txt}{space 1}RAT_Race5_4 {c |}{col 14}{res}{space 2} 1.008484{col 26}{space 2} .0055103{col 37}{space 1}    1.55{col 46}{space 3}0.122{col 54}{space 4} .9977276{col 67}{space 3} 1.019357
{txt}{space 1}RAT_Race5_5 {c |}{col 14}{res}{space 2} 1.006871{col 26}{space 2} .0034398{col 37}{space 1}    2.00{col 46}{space 3}0.045{col 54}{space 4} 1.000143{col 67}{space 3} 1.013645
{txt}{space 3}1.BornUSA {c |}{col 14}{res}{space 2} 1.587274{col 26}{space 2} .0551827{col 37}{space 1}   13.29{col 46}{space 3}0.000{col 54}{space 4} 1.482587{col 67}{space 3} 1.699353
{txt}{space 1}RAT_BornUSA {c |}{col 14}{res}{space 2} 1.008739{col 26}{space 2} .0038709{col 37}{space 1}    2.27{col 46}{space 3}0.024{col 54}{space 4} 1.001171{col 67}{space 3} 1.016364
{txt}{space 12} {c |}
{space 4}MarStat5 {c |}
{space 10}2  {c |}{col 14}{res}{space 2} 2.282769{col 26}{space 2} .0495047{col 37}{space 1}   38.06{col 46}{space 3}0.000{col 54}{space 4} 2.187651{col 67}{space 3} 2.382022
{txt}{space 10}3  {c |}{col 14}{res}{space 2} 3.085089{col 26}{space 2} .0456101{col 37}{space 1}   76.20{col 46}{space 3}0.000{col 54}{space 4} 2.996863{col 67}{space 3} 3.175913
{txt}{space 10}4  {c |}{col 14}{res}{space 2}   .92029{col 26}{space 2} .0839742{col 37}{space 1}   -0.91{col 46}{space 3}0.363{col 54}{space 4} .7693998{col 67}{space 3} 1.100772
{txt}{space 10}5  {c |}{col 14}{res}{space 2} 2.240449{col 26}{space 2} .0398755{col 37}{space 1}   45.32{col 46}{space 3}0.000{col 54}{space 4} 2.163543{col 67}{space 3}  2.32009
{txt}{space 12} {c |}
RAT_MarSta~2 {c |}{col 14}{res}{space 2} .9633125{col 26}{space 2} .0153985{col 37}{space 1}   -2.34{col 46}{space 3}0.020{col 54}{space 4} .9335612{col 67}{space 3}  .994012
{txt}RAT_MarSta~3 {c |}{col 14}{res}{space 2} 1.036899{col 26}{space 2} .0076561{col 37}{space 1}    4.91{col 46}{space 3}0.000{col 54}{space 4} 1.021981{col 67}{space 3} 1.052033
{txt}RAT_MarSta~4 {c |}{col 14}{res}{space 2} 1.031922{col 26}{space 2} .0206075{col 37}{space 1}    1.57{col 46}{space 3}0.116{col 54}{space 4}  .992261{col 67}{space 3} 1.073168
{txt}RAT_MarSta~5 {c |}{col 14}{res}{space 2} .9916891{col 26}{space 2} .0029995{col 37}{space 1}   -2.76{col 46}{space 3}0.006{col 54}{space 4} .9858198{col 67}{space 3} .9975933
{txt}{space 1}Rat_Poverty {c |}{col 14}{res}{space 2} .9969708{col 26}{space 2} .0023473{col 37}{space 1}   -1.29{col 46}{space 3}0.198{col 54}{space 4} .9923748{col 67}{space 3} 1.001588
{txt}{space 1}Rat_Mig_Cum {c |}{col 14}{res}{space 2} .1579796{col 26}{space 2} .0734777{col 37}{space 1}   -3.97{col 46}{space 3}0.000{col 54}{space 4} .0634127{col 67}{space 3} .3935735
{txt}{space 5}Pop_Den {c |}{col 14}{res}{space 2} 1.000007{col 26}{space 2} 5.62e-06{col 37}{space 1}    1.21{col 46}{space 3}0.225{col 54}{space 4} .9999958{col 67}{space 3} 1.000018
{txt}{space 1}Rat_GC_ProE {c |}{col 14}{res}{space 2} 1.000007{col 26}{space 2} 8.97e-06{col 37}{space 1}    0.78{col 46}{space 3}0.437{col 54}{space 4} .9999894{col 67}{space 3} 1.000025
{txt}{space 1}Rat_GC_ProM {c |}{col 14}{res}{space 2} 1.000003{col 26}{space 2} .0000111{col 37}{space 1}    0.27{col 46}{space 3}0.784{col 54}{space 4} .9999812{col 67}{space 3} 1.000025
{txt}{space 1}Rat_GC_ProB {c |}{col 14}{res}{space 2} .9999386{col 26}{space 2} .0000433{col 37}{space 1}   -1.42{col 46}{space 3}0.156{col 54}{space 4} .9998536{col 67}{space 3} 1.000024
{txt}{space 2}Rat_GC_Jew {c |}{col 14}{res}{space 2} 1.000085{col 26}{space 2}  .000073{col 37}{space 1}    1.16{col 46}{space 3}0.246{col 54}{space 4} .9999416{col 67}{space 3} 1.000228
{txt}{space 2}Rat_GC_Oth {c |}{col 14}{res}{space 2} .9999261{col 26}{space 2} .0000246{col 37}{space 1}   -3.00{col 46}{space 3}0.003{col 54}{space 4} .9998777{col 67}{space 3} .9999744
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0001817{col 26}{space 2}  .000128{col 37}{space 1}  -12.23{col 46}{space 3}0.000{col 54}{space 4} .0000456{col 67}{space 3} .0007238
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p 0 6 2}Note: {res:_cons} estimates baseline odds{txt}.{p_end}
{com}.         estimates store m1 
. 
.         if (`model_version' <= 4){c -(}
.                 mchange `margin_demographics', amount(all) delta(100) statistics(all) decimals(7)

{res}svy logit: Changes in Pr(y) | Number of obs = 11763920

{txt}Expression: Pr(Suic), predict(pr)
{res}
{txt}{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Change}{space 1}{space 1}{ralign 9:p-value}{space 1}{space 1}{ralign 9:LL}{space 1}{space 1}{ralign 9:UL}{space 1}{space 1}{ralign 9:z-value}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001841}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001880}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001801}}}{space 1}{space 1}{ralign 9:{res:{sf:-9.09e+01}}}{space 1}
{space 0}{res:{lalign 13:RAT Female}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000048}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2951861}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000137}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000042}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.05e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0628043}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000045}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.86e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001147}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000181}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001669}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000624}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.31e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000219}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0702799}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000456}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.81e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0648015}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000046}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.85e+00}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000861}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000816}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000906}}}{space 1}{space 1}{ralign 9:{res:{sf: 3.75e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001101}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001048}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001153}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.09e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000750}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000692}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000808}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.54e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000239}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000192}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000287}}}{space 1}{space 1}{ralign 9:{res:{sf:9.8366029}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000111}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002616}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000171}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000052}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.66e+00}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000351}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000406}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000295}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.24e+01}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0200393}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000056}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.33e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0007529}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000031}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.38e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001081}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001385}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000777}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.99e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000492}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0006566}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000774}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000209}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.42e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000019}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0008155}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000031}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.36e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000039}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0128129}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000069}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.49e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001728}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.77e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001175}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001427}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000924}}}{space 1}{space 1}{ralign 9:{res:{sf:-9.18e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000563}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002115}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000861}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000266}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.72e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001944}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000036}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:-3.74e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0105156}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:2.5636702}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0439394}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000039}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0174510}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0004139}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2628372}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0003111}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0011388}}}{space 1}{space 1}{ralign 9:{res:{sf:1.1203989}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000391}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0504196}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000783}}}{space 1}{space 1}{ralign 9:{res:{sf:1.9589650}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0425456}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000039}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0309751}}}{space 1}
{space 0}{res:{lalign 13:Race5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001098}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001139}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001056}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.21e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000022}}}{space 1}{space 1}{ralign 9:{res:{sf:0.8692012}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000243}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000287}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1647193}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000585}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000690}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000480}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.09e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000876}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000949}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000803}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.35e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001120}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000855}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001385}}}{space 1}{space 1}{ralign 9:{res:{sf:8.2838511}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000513}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000409}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000616}}}{space 1}{space 1}{ralign 9:{res:{sf:9.7215613}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000222}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000151}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000293}}}{space 1}{space 1}{ralign 9:{res:{sf:6.1756424}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000607}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000356}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000894}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000320}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.16e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000898}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001179}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000617}}}{space 1}{space 1}{ralign 9:{res:{sf:-6.28e+00}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000291}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000404}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000178}}}{space 1}{space 1}{ralign 9:{res:{sf:-5.04e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0016903}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:3.1491866}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0026904}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:3.0092644}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000643}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0115424}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000144}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001142}}}{space 1}{space 1}{ralign 9:{res:{sf:2.5309246}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000369}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0052238}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000110}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000628}}}{space 1}{space 1}{ralign 9:{res:{sf:2.7996140}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0026432}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:3.0146939}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0061076}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:2.7483277}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0067426}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:2.7154691}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0002274}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0761643}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000240}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0004788}}}{space 1}{space 1}{ralign 9:{res:{sf:1.7753951}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001146}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0369413}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000070}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002223}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0894610}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0064912}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:2.7281313}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1150838}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5772462}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1242578}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5385555}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001955}}}{space 1}{space 1}{ralign 9:{res:{sf:0.2968526}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001721}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0005631}}}{space 1}{space 1}{ralign 9:{res:{sf:1.0438010}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000250}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1448152}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000086}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000586}}}{space 1}{space 1}{ralign 9:{res:{sf:1.4593355}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1226781}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000003}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5450546}}}{space 1}
{space 0}{res:{lalign 13:RAT Race5 5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0350579}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000001}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000018}}}{space 1}{space 1}{ralign 9:{res:{sf:2.1108211}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0464367}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:1.9941020}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001448}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1474337}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000512}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003409}}}{space 1}{space 1}{ralign 9:{res:{sf:1.4498809}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000817}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0942753}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000140}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001774}}}{space 1}{space 1}{ralign 9:{res:{sf:1.6749992}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0456953}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:2.0009207}}}{space 1}
{space 0}{res:{lalign 13:BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000563}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000493}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000632}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.59e+01}}}{space 1}
{space 0}{res:{lalign 13:RAT BornUSA}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000005}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000007}}}{space 1}{space 1}{ralign 9:{res:{sf:9.5438893}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0243425}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:2.2554120}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0002043}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1303720}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000605}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0004691}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5139937}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000578}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0112357}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000131}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001025}}}{space 1}{space 1}{ralign 9:{res:{sf:2.5404246}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0237317}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:2.2652216}}}{space 1}
{space 0}{res:{lalign 13:MarStat5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001199}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001114}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001283}}}{space 1}{space 1}{ralign 9:{res:{sf: 2.78e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001948}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001882}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002014}}}{space 1}{space 1}{ralign 9:{res:{sf: 5.79e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000075}}}{space 1}{space 1}{ralign 9:{res:{sf:0.3444480}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000229}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000080}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.9459015}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001159}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001103}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001215}}}{space 1}{space 1}{ralign 9:{res:{sf: 4.07e+01}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000749}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000651}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000848}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.49e+01}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001273}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001447}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001100}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.44e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000040}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4469387}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000142}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000062}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.7608559}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0002023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0002179}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001866}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.54e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000789}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000869}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000709}}}{space 1}{space 1}{ralign 9:{res:{sf:-1.93e+01}}}{space 1}
{space 0}{space 0}{ralign 12:5 vs 4}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0001234}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001081}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001386}}}{space 1}{space 1}{ralign 9:{res:{sf: 1.59e+01}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~2}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
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{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000054}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0173980}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000099}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000010}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.38e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0001439}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001550}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001327}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.53e+01}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000419}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0206348}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000773}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000064}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.32e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000055}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0195862}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000101}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000009}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.34e+00}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~3}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000037}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000000}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000046}}}{space 1}{space 1}{ralign 9:{res:{sf:7.7625057}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000054}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000017}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000077}}}{space 1}{space 1}{ralign 9:{res:{sf:4.8133967}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0053126}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1830286}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0025120}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0131373}}}{space 1}{space 1}{ralign 9:{res:{sf:1.3324951}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000521}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000014}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000311}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000732}}}{space 1}{space 1}{ralign 9:{res:{sf:4.8548254}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000053}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000032}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000075}}}{space 1}{space 1}{ralign 9:{res:{sf:4.9009147}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~4}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000044}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0959455}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000008}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000095}}}{space 1}{space 1}{ralign 9:{res:{sf:1.6665497}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000047}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1211889}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000107}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5512413}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0032421}}}{space 1}{space 1}{ralign 9:{res:{sf:0.6294986}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0099426}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0164268}}}{space 1}{space 1}{ralign 9:{res:{sf:0.4825949}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000222}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1243485}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000061}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000506}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5381843}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000046}}}{space 1}{space 1}{ralign 9:{res:{sf:0.1154261}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000011}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000104}}}{space 1}{space 1}{ralign 9:{res:{sf:1.5757595}}}{space 1}
{space 0}{res:{lalign 13:RAT MarSta~5}}{c |}{space 11}{space 11}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000016}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0268343}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000002}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.22e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0057778}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.77e+00}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000834}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000190}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0001215}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000453}}}{space 1}{space 1}{ralign 9:{res:{sf:-4.30e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000366}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0038409}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000613}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000118}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.90e+00}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:-0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0059832}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000021}}}{space 1}{space 1}{ralign 9:{res:{sf:-0.0000004}}}{space 1}{space 1}{ralign 9:{res:{sf:-2.76e+00}}}{space 1}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:Std Err}{space 1}{space 1}{ralign 9:From}{space 1}{space 1}{ralign 9:To}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}{hline 11}
{space 0}{res:{lalign 13:Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:1 vs 0}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000020}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002446}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000605}}}{space 1}
{space 0}{res:{lalign 13:RAT Female}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000046}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003191}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0003144}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001452}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000266}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000327}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000121}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001614}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001396}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000012}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:AgeGrp4}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:2 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000023}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001624}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000027}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001863}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000029}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000763}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001513}}}{space 1}
{space 0}{space 0}{ralign 12:3 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000024}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001624}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001863}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000030}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001624}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001513}}}{space 1}
{space 0}{space 0}{ralign 12:4 vs 3}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000028}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001863}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001513}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~2}}{c |}{space 11}{space 11}{space 11}
{space 0}{space 0}{ralign 12:0 to 1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000013}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002306}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0002275}}}{space 1}
{space 0}{space 0}{ralign 12:+1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001454}}}{space 1}
{space 0}{space 0}{ralign 12:+delta}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000155}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0000393}}}{space 1}
{space 0}{space 0}{ralign 12:Range}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000144}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001699}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001208}}}{space 1}
{space 0}{space 0}{ralign 12:Marginal}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.0000006}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}{space 1}{ralign 9:{res:{sf:       .z}}}{space 1}
{space 0}{res:{lalign 13:RAT AgeGrp~3}}{c |}{space 11}{space 11}{space 11}
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{res}
{txt}{p 0 0 2}Average predictions{p_end}

{space 0}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:0}{space 1}{space 1}{ralign 9:1}{space 1}
{space 0}{hline 13}{c   +}{hline 11}{hline 11}
{space 0}{space 0}{ralign 12:Pr(y|base)}{space 1}{c |}{space 1}{ralign 9:{res:{sf:0.9998526}}}{space 1}{space 1}{ralign 9:{res:{sf:0.0001474}}}{space 1}

{col 1}1: Delta equals 100.
{com}.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. if (`model_version' == 5 | `model_version' == 6) {c -(}
.         estimates restore m1
.         margin_interact std_same_prop_Sex Female 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_AgeGrp4 AgeGrp4 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_Race5 Race5 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_BornUSA BornUSA 5 `model'
.         estimates restore m1
.         margin_interact std_same_prop_MarStat5 MarStat5 5 `model'
. 
.         if (`model_version' == 6) {c -(}
.                 estimates restore m1
.                 margin_interact std_same_prop_UnEmpl UnEmpl 5 `model'
.                 estimates restore m1
.                 margin_interact std_same_prop_PhysProb PhysProb 5 `model'
.         {c )-}
. 
. {c )-}
{txt}
{com}. 
. 
. log close 
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/N/project/suicide_study/pnas_replication/results/log/logit_1.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}18 Aug 2020, 08:18:19
{txt}{.-}
{smcl}
{txt}{sf}{ul off}